Biopharma 4.0: Combining the Old with the New

Progress in bioprocessing tends to occur through large, discrete steps followed by years or decades of fine-tuning. This “paradigm” model of industrial evolution parallels the ideas in philosopher Thomas Kuhn’s The Structure of Scientific Revolutions. Examples include the use of immortalized mammalian cells in biomanufacturing, single-use bioprocessing, and the discovery of the polymerase chain reaction. These developments, which have undergone countless person-years of refinement, share the similarity of having originated in a breakthrough experiment or idea based either on a critically relevant scientific or engineering principle. Things happen when the science is real, tangible, and actionable.

Paradigms arising purely from need, desire, or regulatory decree have not fared nearly as well, for example personalized medicine, process analytic technology, and quality by design. While these goals are highly desirable and companies exert a lot of effort in achieving them, their realization has been uneven.

Advanced biomanufacturing—Biopharma 4.0—is a hybrid approach as it seeks to integrate old technologies such as continuous processing, automation, and process analytics, with newer approaches such as digitalization, advanced data management, and predictive modeling.

Where to start?

Interest in advanced or next-generation bioprocessing, Biopharma 4.0, is peaking just in time for the upcoming post-pandemic boom. Unlike earlier, narrowly focused initiatives, e.g., process analytics, or approaches that have almost entirely disappeared from consciousness (Six Sigma comes to mind), Biopharma 4.0 aims at a deep overhaul that promises to transform every aspect of biomanufacturing. In many ways, 4.0 incorporates several earlier goals related to monitoring, data, and quality, and seeks to tie them together with the aims of reducing manufacturing costs while improving reliability and quality. Structurally, Biopharma 4.0 seeks to looks like this:

• Continuous processing upstream and downstream through steady-state perfusion and continuous chromatography, respectively, with integration between the two (normally distinct and operationally separate) sets of unit operations
• Automation and digitalization through closed-loop feedback control and big data management and analysis
• Real-time monitoring through spectroscopy, and both hard and soft sensors
• Predictive modeling based on monitoring through cell culture media design and the use of “digital twins” (virtual representations of actual processes)

If this seems like a basket full of everything we’ve been hearing from the commentariat for the last 20 years, that’s because it is. Yet in the current context of rapid product approvals and an explosion in advanced (i.e., cell- and gene-based) therapies, it appears that bioprocessing is finally ready to adopt ways of doing business that other process industries have long ago taken for granted.

The question is where to start.

“Although some of these various innovations are related, and some are even complementary, there is no particular sequence required,” says Tom Fletcher, R&D Director, Process Development at FUJIFILM Irvine Scientific. “Each component stands alone as a way to improve a bioprocess.”

Fletcher adds that companies can prioritize these steps based on which aspects of existing processes require the most improvement, which innovation(s) are the easiest to implement given a company’s specific situation, and which options are most rapidly deployed.

“If choosing just one or two of the easiest or best possibilities in terms of return on investment, I would say big data analysis and management can be achieved simply by hiring specialists, purchasing software, and fine-tuning methods. Similarly with closed-loop feedback control, which may be restricted to most important control parameters.”

Second-tier low-hanging solutions include continuous upstream perfusion culture, and predictive modeling of cell culture (including the use of digital twins). Finally, toward the bottom of the list we find spectroscopy and sensors, continuous downstream processing, and the integration of upstream and downstream continuous processing.

A daunting transformation

Can a new approach, or philosophy, toward 4.0 adoption make a difference? Merrilee Whitney, who heads MilliporeSigma’s BioContinuum™ Platform, believes that the convergence of process and digital technologies will be the key to success. BioContinuum is the company’s future-looking “convergent evolution toward bioprocessing 4.0” suite of products.

But embarking on this “digital transformation” will be daunting, she says, requiring “not only the digital technology itself, but also a shift in mindset and culture of an organization to drive the paradigm shift toward automation.”

MilliporeSigma describes its Bio4C™ Software Suite, for example, as an “ecosystem of software and analytical technologies...to help customers enter the digital era with confidence.” “Our platform helps customers rapidly implement and adopt automation but does not require a heavy existing digital footprint,” Whitney says.

Given the multiple steps and components of Biopharma 4.0, and the dozens of related product or service options, companies rightfully wonder where to start.

To that point real-time data accessibility and visibility represent key low-hanging fruit in the transformation to Biopharma 4.0, according to Whitney. “Biopharmaceutical manufacturers have large volumes of complex data or big data from multiple unconnected systems, sensors, and databases. The ability to contextualize and analyze this data delivers meaningful, actionable smart data.”

The keyword for reluctant or timid 4.0 adopters is flexibility, which allows customers to adopt these ideas stepwise as their needs dictate, and to expand the software as their needs mature and as new innovations become available.

We have heard many of these justifications before, for example with respect to earlier industry-wide initiatives like QbD, PAT, and personalized medicine, which have never seemed to reach levels of adoption to match the hype. Is this time different?

“Even before the pandemic, biopharma market trends had shifted significantly with an increased demand for cost reduction, quality improvements, and flexibility,” Whitney says. “To meet the goals outlined in the BioPhorum Operations Group’s technology roadmap, a paradigm shift was required across process technologies, but more so in the adoption of PAT and digital solutions.”

Biopharma 4.0 seeks to combine revelations from the last three decades of biomanufacturing with new approaches through integration strategies. Its realization, however, relies on meaningful process analytics, advanced data-based control and optimization, and continuous processing—all of which have been possible for a very long time yet do not, despite proven benefits, enjoy anything close to universal adoption. Because 4.0’s technologic ingredients require serious commitments of time and resources, and given the current priority of time-to-market over almost anything else, it is unlikely that many firms will conduct end-to-end operations wholly under 4.0 for the foreseeable future.

By then who knows, perhaps Six Sigma will make a comeback.